31 citations · 68 across the 19 of their papers we have counts for
21 papers
Indirect Influences of Artificial Intelligence Advice on Complex Sepsis Treatment Decisions
Venkatesh Sivaraman, Joel Levin, Eric P. Mason +6
Sepsis is a major public health concern, and many sepsis patients die due to ineffective hemodynamic management. Artificial intelligence could support hemodynamic decisions, partic…
"I Know Where to Look," But Does the LLM? Charting the Gaps Between Clinical Expert Needs and Unstructured Data Abstraction Tools
Venkatesh Sivaraman, Rigney Turnham, George Bonano +16
Clinical data abstraction, the process of distilling structured information from patient records, plays a key role in advancing knowledge about diseases such as cancer. Information…
Expert Evaluation of Clinical AI Tools on Real Point-of-Care Clinical Queries
Jean Feng, Vishal Patel, Patrick Heagerty +5
Physicians now pose millions of clinical questions to AI tools each week, yet these tools are evaluated largely on hypothetical or exam-style questions, not those actually asked in…
Adaptive auditing of AI systems with anytime-valid guarantees
Siyu Zhou, Patrick Vossler, Venkatesh Sivaraman +2
A major bottleneck in characterizing the failure modes of generative AI systems is the cost and time of annotation and evaluation. Consequently, adaptive testing paradigms have gai…
From Fuzzy to Formal: Scaling Hospital Quality Improvement with AI
Patrick Vossler, Jean Feng, Venkat Sivaraman +9
Hospital Quality Improvement (QI) plays a critical role in optimizing healthcare delivery by translating high-level hospital goals into actionable solutions. A critical step of QI…
More Than "Means to an End": Supporting Reasoning with Transparently Designed AI Data Science Processes
Venkatesh Sivaraman, Patrick Vossler, Adam Perer +2
Generative artificial intelligence (AI) tools can now help people perform complex data science tasks regardless of their expertise. While these tools have great potential to help m…
Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisions
Venkatesh Sivaraman, Eric P. Mason, Mengfan Ellen Li +4
Artificial intelligence (AI)-based decision support systems can be highly accurate yet still fail to support users or improve decisions. Existing theories of AI-assisted decision-m…
TempoQL: A Readable, Precise, and Portable Query System for Electronic Health Record Data
Ziyong Ma, Richard D. Boyce, Adam Perer +1
Electronic health record (EHR) data is an essential data source for machine learning for health, but researchers and clinicians face steep barriers in extracting and validating EHR…
Over-Relying on Reliance: Towards Realistic Evaluations of AI-Based Clinical Decision Support
Venkatesh Sivaraman, Katelyn Morrison, Will Epperson +1
As AI-based clinical decision support (AI-CDS) is introduced in more and more aspects of healthcare services, HCI research plays an increasingly important role in designing for com…
Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Tasks
Venkatesh Sivaraman, Anika Vaishampayan, Xiaotong Li +4
Temporal predictive models have the potential to improve decisions in health care, public services, and other domains, yet they often fail to effectively support decision-makers. P…
Static Algorithm, Evolving Epidemic: Understanding the Potential of Human-AI Risk Assessment to Support Regional Overdose Prevention
Venkatesh Sivaraman, Yejun Kwak, Courtney Kuza +6
Drug overdose deaths, including those due to prescription opioids, represent a critical public health issue in the United States and worldwide. Artificial intelligence (AI) approac…
Divisi: Interactive Search and Visualization for Scalable Exploratory Subgroup Analysis
Venkatesh Sivaraman, Zexuan Li, Adam Perer
Analyzing data subgroups is a common data science task to build intuition about a dataset and identify areas to improve model performance. However, subgroup analysis is prohibitive…
Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments
Angie Boggust, Venkatesh Sivaraman, Yannick Assogba +3
To deploy machine learning models on-device, practitioners use compression algorithms to shrink and speed up models while maintaining their high-quality output. A critical aspect o…
How Consistent are Clinicians? Evaluating the Predictability of Sepsis Disease Progression with Dynamics Models
Unnseo Park, Venkatesh Sivaraman, Adam Perer
Reinforcement learning (RL) is a promising approach to generate treatment policies for sepsis patients in intensive care. While retrospective evaluation metrics show decreased mort…
Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care Unit
Nur Yildirim, Susanna Zlotnikov, Deniz Sayar +14
Advances in artificial intelligence (AI) have enabled unprecedented capabilities, yet innovation teams struggle when envisioning AI concepts. Data science teams think of innovation…
Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care
Venkatesh Sivaraman, Leigh A. Bukowski, Joel Levin +2
Artificial intelligence (AI) in healthcare has the potential to improve patient outcomes, but clinician acceptance remains a critical barrier. We developed a novel decision support…
LSTM-RASA Based Agri Farm Assistant for Farmers
Narayana Darapaneni, Selvakumar Raj, Raghul V +3
The application of Deep Learning and Natural Language based ChatBots are growing rapidly in recent years. They are used in many fields like customer support, reservation system and…
Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support
Anna Kawakami, Venkatesh Sivaraman, Hao-Fei Cheng +7
AI-based decision support tools (ADS) are increasingly used to augment human decision-making in high-stakes, social contexts. As public sector agencies begin to adopt ADS, it is cr…
Emblaze: Illuminating Machine Learning Representations through Interactive Comparison of Embedding Spaces
Venkatesh Sivaraman, Yiwei Wu, Adam Perer
Modern machine learning techniques commonly rely on complex, high-dimensional embedding representations to capture underlying structure in the data and improve performance. In orde…
TextEssence: A Tool for Interactive Analysis of Semantic Shifts Between Corpora
Denis Newman-Griffis, Venkatesh Sivaraman, Adam Perer +2
Embeddings of words and concepts capture syntactic and semantic regularities of language; however, they have seen limited use as tools to study characteristics of different corpora…
Pulse of the Pandemic: Iterative Topic Filtering for Clinical Information Extraction from Social Media
Julia Wu, Venkatesh Sivaraman, Dheekshita Kumar +2
The rapid evolution of the COVID-19 pandemic has underscored the need to quickly disseminate the latest clinical knowledge during a public-health emergency. One surprisingly effect…